Australian Building Analytics Lab

New information and communication technologies are placing a massive and rapidly growing volume of data at our disposal. The construction and property industry generally fails to utilise the available data effectively to improve understanding and decision-making processes. Complex, timely and dynamic decision making is undertaken at the project, operational and strategic level by all organisations. Analytics is a powerful new business concept that seeks to promote more evidence-based and systematic decision-making using a variety of established and emerging data analysis techniques.

The Australian Building Analytics Lab is seeking to develop and implement new analytics capabilities relevant to construction and property. The primary focus is on business analytics, which involves sourcing, storing, analysing and interpreting data collected from multiple business processes and combined into a dynamic decision-support system. The key innovation is in how data from multiple sources can be aggregated and the resulting very large datasets analysed using novel statistical techniques. Advanced visualisation techniques are then applied to interrogate and make sense of the data as it is sliced and diced along key performance metrics. This approach is already proving successful in other industry sectors such as aviation, travel, and finance to improve organisational performance and create a sustainable competitive advantage.

A secondary focus is on the specific, significant capabilities of a particular digital technology – the video game engine. A video game engine is the kernel of coding used to drive a collection of actual game implementations. Extremely high performance graphics capabilities are used to render moving photorealistic scenes in real time 3D. Of particular interest is the rapidly developing capacity for game engines to harvest, process and represent to the user in real time a myriad of data input sources ranging from live video streams, spatial scanning devices, web feeds, databases, building information models, blogs, third party software and largely anything digital. This approach is entirely novel and presents an opportunity to immerse decision makers in a digital world of data that is interactive and directly related to real world settings using virtual reality technologies.

The use of CryENGINE as an analytics engine is unique to our work. We see a broad and compelling scope for the application of such analytics engines, to probabilistic cost control, design generation, development planning, user engagement and more.

Please refer to the demonstration projects for examples, or contact us to discuss how an analytics engine technology might improve your key performance outcomes.

 

Research Projects

A physical and economic study using a site-specific urban development analytics engine

2011-2012

This project examined the capacity for graphical representation of data to promote the active involvement of groups of people in the analysis process itself. For a physical and economic study of a site-specific urban development, the analytics engine mines, harvests, analyses and visualises a range of data relevant to the particular development proposal. This potentially includes captured geometry of the site context, planning regulations on height and site density, occupancy figures for related and neighbouring properties, potential site lines, building costs, building construction materials, tenancies, pedestrian counts, street furniture, vehicle usage patterns, rental values, etc. This project seeks to extract and/or generate data relevant to a particular decision at a particular time in a particular location.

 

Opportunities

The Building Value project is seeking key stakeholder engagement in the development of an evidence-based approach to building design, procurement and operation. Specifically we are seeking access to any relevant data you might provide that could inform a broad and whole-of-life factor consideration. This may include, but is not limited to, construction cost, time, variations, defects, material and labour supply, operational costs, user satisfaction, environmental factors, product quality, etc. Our initial focus is on the broad education sector, but we are also interested in construction project data from other sectors.

In return for the provision of data we will work with you to identify the kinds of data analysis that might be possible and of interest to you. Benchmarking your data against other data sets (in aggregate) is certainly one of the analyses we would undertake to perform on your behalf. Cross sector and international comparisons are also intended. The aim is to provide a raft of the criteria you associate with value for money and deliver a dashboard of metrics and benchmarks specific to those criteria.

All data provided will be treated as commercial-in-confidence and will only be incorporated into broadly aggregated statistics with your permission. We have the resources to compile and extract data directly from your system to minimise the imposition on your organisation.

If you are interested in research study related to our activities, please forward your research proposal for our consideration.

- See more at: http://www.be.unsw.edu.au/programs/building-value/opportunities#sthash.i...

The Australian Building Analytics Lab is seeking key stakeholder engagement in the development of an evidence-based approach to the business of construction and property. Specifically, we are seeking access to any relevant datasets you might provide that could be used to demonstrate the potential of analytics to your business bottom line. Data could relate to any aspect of the business activity and may be supplemented with external data sources. Our initial focus is on the application of advanced statistical analysis of large datasets and novel visualisation techniques to benchmark and forecast decision making.

In return for the provision of data we will work with you to identify the kinds of data analysis that might be possible and of interest. The aim is to generate a raft of key decision-making metrics that can be represented as a visual dashboard of benchmarks and performance directly from live data feeds.

All data provided will be treated as commercial-in-confidence and will only be incorporated into broadly aggregated statistics with your permission.

If you are interested in exploring the potential of analytics specific to construction and property, please contact us on: buildinganalytics@unsw.edu.au

Contact Us
Co-Directors:

Benson Lim

Imriyas Kamardeen

Russell Lowe

Sidney Newton

Phone +61 2 9385 6144

Fax     +61 2 9385 4507

Email buildinganalytics@unsw.edu.au

 

Associated Researchers:

Jinu Kim

Martin Loosemore

Bee Lan Oo

Riza Sunindijo

Cynthia Wang

 

Research Assistants:

Siti Salwa Mohd Ishak

Mohammad Mojtahedi

Rui Wang

 

Research Students:

Diane Christina

 

Publications

Zhang, L., Zhou, P., Newton, S., Fang, J., Zhou, D. and Zhang, L. (2015) Evaluating clean energy alternatives for Jiangsu, China: An improved multi-criteria decision making method, Energy, Vol.90, pp.953-964.

Mojtahedi, M. and Newton, S. (2015) An analysis of the factors that determine the economic impact of flooding on road transport infrastructure in Australia, Proceedings Cobra International Conference 2015, Sydney, pp.1-11.

Mojtahedi, M. and Newton, S. (2015) Stakeholder Proactive Approach in Floodplain Risk Management in NSW Built Environment, Proceedings FMA National Conference 2015, Brisbane, pp.1-11.

Newton, S., Skitmore, RM. and Love, P. (2014) Managing Uncertainty to Improve the Cost Performance of Complex Infrastructure Projects, in D. Amaratunga, R. Haigh, L. Ruddock, K. Keraminiyage, U. Kulatunga and C. Pathirage (editors), Construction in a Changing World, Online CIB, No.579, pp.1-12.

Ishak, SSM. and Newton, S. (2012) Taking a Broader View of user Resistance to online Project Information Management Systems Implementation in the Construction Industry, in I. Kamardeen, S. Newton, B. Lim and M. Loosemore (eds), Proceedings of 37th AUBEA International Conference, Sydney: UNSW, pp.647-656.

Newton, S. (2012) Improving a Cost Estimate Using Reference Class Analytics, in Proceedings of Management of Construction: Research to Practice, Volume 1, Montreal, Canada: CIB, pp.73-83.

Newton, S. and Goldsmith, R. (2011) An Analysis of Stakeholder Preferences for Threshold Learning Outcomes in Construction Management in Australia, in Egbu, C. and Lou, E.C.W. (Eds.), Proceedings of 27th Annual ARCOM Conference: Vol.1, Bristol, UK: Association of Researchers in Construction Management, pp.127-136.

Newton, S. and Lowe, R. (2011) Using an Analytics Engine to Understand the Design and Construction of Domestic Buildings, in L. Ruddock and P. Chynoweth (eds), Proceedings of RICS Construction and Property Conference, COBRA 2011, Manchester: RICS, pp.410-419.